Throughout my career, I've managed the delivery of hundreds of causal attitudinal studies to assess advertising impact on consumer perceptions. At Pinterest, I oversee our comprehensive brand lift measurement suite, which includes both our native 2Q and 6Q solutions as well as partnership with Kantar. These studies utilize native in-app polling to measure the delta between exposed and control groups, allowing advertisers to understand the incremental impact of their campaigns on brand metrics. This work requires deep expertise in survey design, methodology, and cross-functional collaboration to ensure reliable results.

Twitter’s first-party Conversation Lift campaign measurement was built upon Twitter’s Intent-to-Treat methodological foundation and aimed to capture the leading mid-funnel indicators of favorability and top-of-mind awareness by evaluating the extent to which people exposed to paid campaigns on Twitter created more—or more favorable—content after seeing an ad. From auto brands launching new models to entertainment brands launching new shows and movies, my team and I deployed these studies to help clients understand the extent to which campaigns increased their cultural relevance.

Building on my experience developing location lift studies with Placed/Foursquare at Twitter, I now oversee Pinterest's partnerships with both Foursquare and NinthDecimal for foot traffic measurement. These solutions match Pinterest campaign exposure data with first-party device location data to quantify the incremental impact of advertising on store visits. NinthDecimal's Location Conversion Index (LCI™) and Foursquare's measurement capability allow us to demonstrate Pinterest's effectiveness in driving physical retail results.

I've been deeply involved in conversion measurement throughout my career, from alpha testing Twitter's first Conversion Lift studies to now overseeing Pinterest's sophisticated conversion measurement solutions. At Pinterest, we offer Conversion Lift studies that track both online and offline conversion activity, using statistical inference methods to measure incremental ROAS and CPA. We complement this with Pinterest Conversion Analysis, which provides deeper insights into conversion windows, time-to-convert metrics, and conversion funnel analysis. This can be enriched with customer segmentation data to compare Pinterest vs. non-Pinterest conversions.

I was directly responsible for the selection, implementation, launch, and ongoing refinement of Twitter’s Incremental Viewership measurement solutions. Originally referred to as “TV Tune-In” measurement in partnership with either Nielsen or SambaTV, our offering eventually grew to serve Television, Streaming, Sports League, and Premium Video On-Demand advertisers. This solution was vital to the Entertainment sales team’s strategy to move Twitter out of its experimental advertising budgets and into a position as a flagship partner proven to drive results.

By the end of my time at Twitter, our most popular incremental viewership offering was built on top of Twitter’s intent-to-treat methodological framework, offering a causal look at how exposure to promoted Tweets delivered incremental viewers to the advertiser and provided performance split by targeting creative and flighting strategies. In addition to these standard offerings, these reports integrated with our A/B testing rollout and even showcased the viewership behavior among those exposed to second-hand earned media generated by people exposed to the paid campaign (sometimes referred to as “paid-earned” exposure).

At Pinterest, I'm expanding beyond my previous experience to lead innovative measurement approaches that combine multiple data sources and methodologies. This includes integrating channel-attributed data, customer segmentation analysis, and conversion path analysis to provide advertisers with a comprehensive view of Pinterest's impact across the consumer journey. My team works closely with retail advertisers to develop custom measurement solutions that align with their specific business objectives and measurement frameworks.

Powered by Oracle Advertising or Nielsen Catalina Solutions, Twitter's closed-loop sales lift reporting enabled advertisers to measure the Return on Advertising Spend (ROAS) from promoted Tweets. This approach involved matching users exposed to ads on Twitter with real-world purchase data—sourced either from loyalty card transactions or compensated research panels. The vendors then applied advanced modeling techniques to extrapolate the impact observed in the matched data to the overall purchase behavior of all exposed households. They then compared these findings with those from a synthetic control group. Successfully delivering this measurement demanded not only a grasp of the experimental design's intricacies but also a thorough understanding of the creative, targeting, and flighting strategies proven to boost offline sales.

Drawing from my experience with clean room measurement at Twitter, I now help guide Pinterest's approach to privacy-compliant measurement solutions. This work has become increasingly critical as the industry evolves, requiring careful balance between measurement accuracy and user privacy. We work closely with partners and clients to ensure our measurement solutions meet both technical requirements and privacy standards while delivering actionable insights.

Oracle Advertising’s Buy Through Rate (BTR) reporting offered an observational understanding of automobile purchases among households exposed to in-market automotive advertising on Twitter by matching exposed users to DMV registrations. Although these reports weren’t causal and did not have a control group, they did offer clients an understanding of which targeting techniques were best aligned with likely purchasers, discover the rates at which the reached audience purchased a car from a competitor, as well as other insights about the reached audience. The offering helped capture advertising dollars for Twitter by strengthening the platform’s position as home to affluent households who purchased “more cars, more often.”

I was part of the team that helped deliver Twitter’s first MTA integration with Neustar to key clients in the automotive and entertainment sectors, for which I was responsible. Although the Musk acquisition cut these initiatives short, I had the opportunity to influence the product roadmap, learn about my clients’ attribution models, and provide feedback on the fidelity of our measurement API.

Throughout my time at Twitter, we provided data to service clients’ Marketing Mix Modeling initiatives. Handling these requests often involved intricate data extraction and transmission processes. Yet, the key to success is client discovery and proactive objection handling. This involved understanding how clients structured their models, grasping the granularity at which publishers were analyzed, and assessing how closely clients' on-platform advertising adhered to best practices for driving sales. These efforts were critical for maintaining and expanding Twitter's share of the advertising budget.

Twitter's Amplify program, a critical strategic initiative, aimed to increase revenue by tapping into digital video advertising budgets, which were incremental to the social media advertising budgets to which Twitter was traditionally aligned. Amplify curated, relevant, high-quality, brand-safe video content—ranging from NFL instant replays to The Oscars' red carpet moments—and offered pre-roll ad space to premier advertisers. Despite its popularity, securing rights to high-quality content posed a significant, ongoing challenge.

In collaboration with SambaTV, I spearheaded the development of a custom audience measurement product for our Amplify content acquisition team, providing them with critical data and insights. These reports detailed the size and composition of the audience reached by each content provider and assessed the extent to which this audience was incremental to traditional live, linear viewership. For instance, we could demonstrate to a major sports league how their live instant replays predominantly attracted light TV viewers, boosting their audience size by 20%. Senior leaders at Twitter used these insights in discussions with C-suite entertainment executives during content acquisition and renewal negotiations.

Twitter established a custom server-to-server integration with Nielsen, facilitating cross-publisher Digital Ad Ratings (DAR) dashboards and cross-channel Total Ad Ratings reporting. This integration empowered advertisers to quantify deduplicated audience measurement metrics accurately, gaining deeper insights into campaign performance across various platforms. My team and I played a pivotal role in helping advertisers configure their campaigns for DAR measurement. We guided optimal flighting strategies to maximize results and addressed any objections. Our hands-on approach ensured advertisers leveraged the full potential of Nielsen's measurement capabilities, driving impactful outcomes for their campaigns.